2018/05/11 by Rudra P K Poudel, Rudra P. K. Poudel, Poudel, Rudra P K +6 · 5 citations
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1805.04554
Published as a conference paper at British Machine Vision Conference (BMVC), 2018
openalex publication_date 2018/05/11 · arxiv created 2018/11/05 · arxiv updated 2018/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naive adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps with pipelined computations for streamed data).